Research Article | Open Access

Volume 2025 |Article ID 100095 | https://doi.org/10.1016/j.plaphe.2025.100095

Volumetric Deep Learning-Based Precision Phenotyping of Gene-Edited Tomato for Vertical Farming

Yu-Jin Jeon,^1,2,7 Seungpyo Hong,^3,7 Taek Sung Lee,⁴ Soo Hyun Park,⁴ Giha Song,³ Myeong-Gyun Seo,³ Jiwoo Lee,³ Yoonseo Lim,³ Jeong-Tak An,¹ Sehee Lee,³ Ho-Young Jeong,⁵ Soon Ju Park,⁶ Chanhui Lee,^3,5 Dae-Hyun Jung ,^1,2 Choon-Tak Kwon ^1,3

¹Department of Smart Farm Science, Kyung Hee University, Yongin, 17104, Republic of Korea
²Interdisciplinary Program in IT-Bio Convergence System, Kyung Hee University, Yongin, 17104, Republic of Korea
³Graduate School of Green-Bio Science, Kyung Hee University, Yongin, 17104, Republic of Korea
⁴Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, 25451, Republic of Korea
⁵Department of Plant & Environmental New Resources, Kyung Hee University, Yongin, 17104, Republic of Korea
⁶Division of Applied Life Science, Plant Molecular Biology and Biotechnology Research Center, Gyeongsang National University, Jinju, 52828, Republic of Korea
⁷These authors contributed equally to this work

Abstract

Global climate change and urbanization have posed challenges to sustainable food production and resource management in agriculture. Vertical farming, in particular, allows for high-density cultivation on limited land but requires precise control of crop height to suit vertical farming systems. Tomato, a globally significant vegetable crop, urgently requires mutant varieties that suppress indeterminate growth for effective cultivation in vertical farming systems. In this study, we utilized the CRISPR-Cas9 system to develop a new tomato cultivar optimized for vertical farming by editing the Gibberellin 20-oxidase (SlGA20ox) genes, which are well known for their roles in the “Green Revolution”. Additionally, we proposed a volumetric model to effectively identify mutants through non-destructive analysis of chlorophyll fluorescence. The proposed model achieved over 84 % classification accuracy in distinguishing triple-determinate and slga20ox gene-edited plants, outperforming traditional machine learning methods and 1D-CNN approaches. Unlike previous studies that primarily relied on manual feature extraction from chlorophyll fluorescence data, this research introduced a deep learning framework capable of automating feature extraction in three dimensions while learning the temporal characteristics of chlorophyll fluorescence imaging data. The study demonstrated the potential to classify tomato plants customized for vertical farming, leveraging advanced phenotypic analysis methods. Our approach explores new analytical methods for chlorophyll fluorescence imaging data within AI-based phenotyping and can be extended to other crops and traits, accelerating breeding programs and enhancing the efficiency of genetic resource management.